• Journal of the Korea Institute of Information Security & Cryptology
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• v.12 no.3
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• pp.87-94
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• 2002

This study presents an MSB(Most Significant Bit) Int multiplier using cellular automata, along with a new MSB first multiplication algorithm over GF($2^m$). The proposed architecture has the advantage of high regularity and a reduced latency based on combining the characteristics of a PBCA(Periodic Boundary Cellular Automata) and with the property of irreducible AOP(All One Polynomial). The proposed multiplier can be used in the effectual hardware design of exponentiation architecture for public-key cryptosystem.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.35S no.11
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• pp.88-95
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• 1998

This paper is the result of constructing information processing system such as living creatures' brain based on artificial life techniques. The living things are best information processing system in themselves. One individual is developed from a generative cell. And a species of this individual has adapted itself to the environment through evolution. In this paper, we propose a new method of designing neural networks using biological inspired developmental and evolutionary concept. Ontogeny of organism is embodied in cellular automata(CA) and phylogeny of species is realized by evolutionary algorithms(EAs). We call 'Evolving Cellular Automata Neural Systems' as ECANSI. The connection among cells is determined by the rule of cellular automata. In order to obtain the best neural networks in given environment, we evolve the arragemetn of initial cells. The cell, that is a neuron of neural networks, is modeled on chaotic neuron with firing or rest state like biological neuron. A final output of network is measured by frequency of firing state. The effectiveness of the proposed scheme is verified by applying it to Exclusive-OR and parity problem.